Forgot to turn in on time

Hey Professor,

I had this done on time but forgot to turn it in on time. I usually turn it in during class on Tuesdays as I have done with all the other assignments I have done but I forgot. Could I get the late penalty waived? If not, it is fine. I understand.

Best, Ricky

Assignment: Logistic Regression

Our goal in this assignment is to apply our logistic regression technique to some new samples:

  1. Pulsars: Apply the technique to the pulsar dataset, but using only two variables (which you can pick). Note that in the Logistic Regression notebook we used the Pulsar data to introduce Logistic Regression, we actually applied the Regression method to the University Admission dataset.

  2. MNIST single digit: Apply the Logistic Regression technique to the MINST dataset, using one digit (say 5) as the positive (y=1) class, and another digit (say 7) as the negative (y=0) class.

  3. Extra: Try to do a simple version of a multi-class classification problem using MNIST: use 3 digits. Your primary output should be a confusion matrix. Hint:

    • you will want to loop over the 3 digits, in each case treating that digit as signal, and the others as background.
    • you will thus have 3 logistic regression models.

Task 0: Get methods from logistic regression notebook

You will need the following:

1: Pulsar dataset

You will need to pick two variables to use as your features (since we are basing this on the example University Admission dataset). You will need to:

Extra Credit #1: Apply to MNIST dataset (1 point)

Here we will want to bring in two digits, then split into a train a test sample.

Here you just want to:

NOTE: Make sure you check the number of iterations and the resulting confusion matrix. If there is evidence that your fit is not optimal, you may need to adjust both the learning rate and the delta (downward!).

I made an attempt to do it but got stuck. Here is my effort

i ran out of time

Extra Credit #2: Multi-class classification (2 points)

Try to do a simple version of a multi-class classification problem using MNIST: use 3 digits (example: 5,6,7). Your primary output should be a confusion matrix.

Hint:

The only output expected is a confusion matrix. Ask if you have questions!